There was a time, a little over two years ago, when SQL-on-Hadoop was about cracking open access to Hadoop data for those with SQL skillsets and eliminating the exclusivity of access that Hadoop/MapReduce specialists had on the data. Yes, some architectural details – like whether the SQL engine was hitting the data nodes in the Hadoop cluster directly – were important too. But, for the most part, solutions in the space were neatly summed up by the name: SQL, on Hadoop.
Today, SQL-on-Hadoop solutions are best judged not by their SQL engines per se, but instead by the collaborative scenarios they enable between Hadoop and the conventional data warehouse. Hadoop can be seen as a usurper, peer or peripheral of the data warehouse; the SQL-on-Hadoop engine you use determines which one (or more) of these three roles Hadoop can be implemented to fulfill.
In Gigaom Research’s just-published Sector Roadmap: Hadoop/Data…
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